Current treatment recommendations for patients with diabetes recognize that tight control of glycosylated hemoglobin (A1c) and cardiovascular disease (CVD) risk factors may not be appropriate for complex patients. If the risks of adverse events are greater for these patients and if the benefits of tight control are limited due to shortened life expectancy, there may be little reason to pursue tight control of A1c and CVD risk factors such as elevated low-density lipoprotein (LDL) and blood pressure (BP). To address this, guidelines recommend individualizing treatment for complex patients. unfortunately, little evidence exists to support these individualized treatment decisions. Because most drug treatment trials excluded complex patients, neither the outcomes of tight control nor the effects of the typical drug regimens used to achieve tight control are known. Existing guidelines would be significantly strengthened with this information, but it is extremely unlikely that new studies of tight control or drug treatment trials in complex patients will ever be conducted. This new information is particularly important as treatment guidelines are increasingly used to develop publicly-reported quality metrics and guide pay-for-performance (P4P) efforts. Without an evidence base to modify guidelines appropriately, physician incentives to adhere tightly to recommendations could have perverse effects that might harm patients. Our specific aims examine the role of patient complexity in the relationship between: (1) patients' long-term and short-term control levels for A1c, LDL, and BP and overall health outcomes (ER visits, hospitalizations, and death), (2) long-term and short-term A1c, LDL, and BP control and specific health outcomes (diabetes complications and possible drug-related morbidity), and (3) typical drug regimens used to achieve A1c, LDL, and BP control and possible drug-related morbidity. Our sample includes approximately 8,300 patients with diabetes who were cared for by a large Midwestern multi-specialty physician group during 2003-2012. We link clinical data (e.g., A1c, LDL, BP values) from the electronic health record to administrative data from Medicare and two large local health maintenance organizations (HMOs). Our analytic approach involves the use of marginal structural modeling to take advantage of the longitudinal nature of our data. Overall, the results from our investigation will impact diabetes treatment guidelines, development of quality metrics, construction of pay-for-performance thresholds, and targeting of interventions.